PROJECT SUMMARY Primary angle closure glaucoma (PACG), the most severe form of primary angle closure disease (PACD), is a leading cause of permanent blindness worldwide. Gonioscopy, a qualitative and subjective angle assessment method, is the current clinical standard for diagnosing PACD despite its limited ability to quantify disease severity, especially in patients with early angle closure. Anterior segment optical coherence tomography (AS-OCT) is a quantitative and objective imaging-based method for assessing the angle. However, there are limited methods for clinicians to apply AS-OCT to the care of angle closure patients. The primary objectives of this K23 career development proposal are: 1) to demonstrate the benefit of a quantitative anterior segment OCT-based approach to evaluating patients with PACD and develop classification models for PACD based on AS-OCT measurements; and 2) to provide an academic glaucoma specialist with the training and mentored research experience necessary to conduct independent clinical research. Achieving these objectives will provide critical skills and experiences necessary to establish an independent research program focused on applying AS-OCT imaging to improve the clinical care of patients with PACD. The proposed K23 application will provide additional training in four vital areas: 1) epidemiology and mechanisms of chronic disease; 2) classification of disease and prediction of disease risk; 3) biostatistical methods for clinical research; 4) machine learning and automated data analysis. The proposed research will use population-based data collected as part of the NIH-funded Chinese American Eye Study (CHES) to compare AS-OCT and gonioscopic assessments of angle width and determine the strength of association between these assessments and known PACD risk factors. The relationship between the degree of angle closure, measured by AS-OCT, and intraocular pressure (IOP), a sequela of angle closure and strong risk factor for glaucoma, will be characterized with CHES data and validated with data obtained by using a novel pupil control system on PACD patients recruited from the USC Roski Eye Institute (USCREI). Classification models for PACD stage and severity based on AS-OCT data from CHES will be developed using machine learning algorithms and validated with prospective data from USCREI patients with PACD. The results of the proposed research will provide the foundation for a future longitudinal study examining the benefit of using anterior segment OCT-based assessments and classification models to evaluate PACD patients and deliver targeted treatment to patients with higher risk for PACG. The ultimate goal of my research is to develop quantitative imaging-based diagnostic and treatment protocols that guide the standardized care of PACD patients and decrease the incidence of PACG and its associated ocular morbidity.